Leveraging machine learning and data analytics for equipment reliability in oil and gas using predictive maintenance

Virginia, Ekunke Onyeka and Obada, Okiemute Richards and Oke, Bioluwatife Oluwaferanmi and Jimso, Israel Oluwaseun and Iwalokun, Austine Oluwole and Akinbolusere, Michael Oluwatosin and Pius, Awe Boluwatife (2025) Leveraging machine learning and data analytics for equipment reliability in oil and gas using predictive maintenance. World Journal of Advanced Research and Reviews, 25 (1). pp. 2212-2218. ISSN 2581-9615

The oil and gas industry operates under very extreme conditions, posing a huge challenge when it comes to equipment reliability. Predictive maintenance; which is now possible through machine learning and data analytics, has transformed the way one looks at equipment management by making real-time failure prediction possible, reducing unplanned downtime, and optimizing maintenance schedules. The review of the technological advances in predictive maintenance methodology focuses on supervised and unsupervised machine learning, deep learning models, and integration with IoT-big data analytics. The paper also summarizes a number of case studies from some of the leading IOCs such as Shell, BP, ExxonMobil, Chevron, and Total Energies. While emphasizing respective KPIs between traditional and predictive maintenance methods; advantages, challenges, and future opportunities in the use of predictive maintenance systems were analysed. This review will be very helpful to both academics and field professionals with research and professional interests in pursuing operational efficiency and sustainability for the oil and gas industry.